Reflections on major sport event volunteer legacy research
Bibliographic record
Abstract
Despite the varying attention over time to economic, infrastructure, tourism, political, environmental, social, and health aspects of the legacy of hosting a major sport eventand acknowledging that this may not be an exhaustive listthe human factor persists.This is not surprising given the acknowledged critical roles and vast numbers of volunteers engaged in the hosting of major sport events like the Olympics or FIFA World Cup (IOC, 2013;Nichols & Ralston, 2011;Zhuang & Girginov, 2012).Communities of all sizes across the world host what may be considered, from their per-spective, major sport events. 1 The volunteer workforce is just one investment of the host community in staging an outstanding if not 'world class' event.Certainly, it is critical to ensure the 'heroes of these Games' (IOC, 2012) are effective.However, there is a growing focus on the legacy -'the tangible and intangible structures created for and by a sport event that remain longer than the event itself' (Preuss, 2007, p. 211)of that volunteer effort and engagement (DCMS, 2012;IOC, 2008).The 'carryover effect of ongoing com-munity support' (Doherty, 2009, p. 187) as a legacy may be represented by continued or increased volunteer support for other community special events and enhanced community volunteerism in general, in terms of positive attitudes towards, and increased rates and levels of, volunteering.To that end, scholars continue to investigate the volunteer legacy of major sport events in order to understand the nature of such an effect, as well as its causes and mechanisms, with implications for event bidding and hosting policy, planning, and execution.Doherty's study of the Jeux du Canada Games volunteers, published in this journal in 2009, is but one investigation along this path.At the time of its publication, only a few scholars had examined future volunteering intentions of major sport event volunteers, and almost all of that work was descriptive in nature (Doherty, 2009).Doherty relied on social exchange theory to frame an examination of the impact of the perceived costs and benefits of Games volunteering on future intentions of planning and on-site or Games delivery volunteers.In
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".